Machine learning for predicting measles outbreaks in resource-limited settings

Abstract Background Measles remains a major public health issue, especially in regions with low vaccination rates and limited healthcare access. Timely outbreak prediction is crucial for effective intervention and resource allocation. Traditional epidemiological models struggle with real-time predictions due to reliance on historical data and limited analysis. Artificial intelligence (AI) offers a data-driven approach to capturing complex outbreak patterns and improving prediction accuracy. Methods This study developed and evaluated machine learning (ML) models to predict measles outbreaks at the community level, using real-world data collected by community volunteers in Ethiopia. The dataset includes demographic, vaccination status, location, clinical, and image data, providing a comprehensive assessment of outbreak risk factors. Five ML models-Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), and K-Nearest Neighbors (KNN)-were assessed using metrics such as area under the receiver operating curve (AUC), precision, recall, and F1-score. Results The results demonstrate that machine learning models can effectively predict measles outbreaks, with the top-performing model, K-Nearest Neighbors (KNN), achieving an AUC of 0.87 (95% CI: 0.84-0.90). Among the five models tested, KNN outperformed the others, showing the highest predictive accuracy, and the difference in AUC was statistically significant (p < 0.05). Conclusions The model's ability to capture complex epidemiological factors underscores its potential to enhance surveillance and early warning systems. This research highlights the effectiveness of ML-based measles prediction in resource-limited settings. Integrating these models into public health strategies can improve early detection and reduce measles impact. Future work will focus on refining the model, incorporating real-time data, and expanding evaluation metrics for practical deployment. Key messages • Machine learning enables accurate early measles detection fusing different data sources, supporting faster response and better health outcomes in communities. • Machine learning offers a powerful tool for predicting outbreaks early, filling critical gaps in low-resource health systems.